The present disclosure relates generally to high dynamic range (HDR) imaging techniques and, more specifically, to techniques for aligning digital images acquired for an HDR imaging process.
This section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present techniques, which are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art.
In recent years, digital image capture devices have become increasingly popular due, at least in part, to such devices becoming more portable as well as affordable for the average consumer. Further, in addition to a number of stand-alone digital cameras currently available on the market, it is not uncommon for digital imaging devices to be provided as an integrated component of a consumer electronic device, such as a desktop or notebook computer, a cellular telephone, or a portable media player. With regard to digital image capture devices, high dynamic range (HDR) imaging generally relates to a set of imaging techniques that allows for the the capture and representation of a greater dynamic range of luminances between the lightest and darkest areas of an image than standard digital imaging techniques. Wider dynamic ranges allow for HDR images to more accurately represent the wide range of intensity levels found in real-world scenes and, therefore, produce an image that may be more aesthetically pleasing.
One method for capturing HDR images includes the merging of multiple photographs. For instance, this process may include capturing multiple images of an image scene at different exposures in succession, and then processing them to generate a composite HDR image. When multiple images of the same scene are taken using a digital camera, it may be desirable to shift the images so that corresponding points and objects in the images match. For instance, if images are taken in rapid succession in an automated manner, camera shake, using the camera from a moving platform or vehicle, changes in lighting and exposure, may cause successive images to not be in complete alignment, which may result in motion artifacts in the composite HDR image. Further, if local motion, i.e., trees swaying in the wind, people and faces shifting or moving slightly from frame to frame, etc., is detected during the image alignment process, this detection may indicate that blurring may occur in regions of the image containing the local motion, which may cause the final rendered HDR image aesthetically unsuitable.
While some alignment algorithms currently exist, many existing alignment algorithms are designed and/or optimized for desktop computing applications and processors. As portable mobile imaging devices have become increasingly popular, it has become desirable to scale or port such alignment algorithms to run on mobile processors so that certain features (e.g., HDR imaging) may also be available on mobile devices. However, as mobile processors are generally designed with power consumption constraints in mind (e.g., mobile devices are typically powered by a limited power source, such as a battery), they often have lower clock speeds and computing power relative to faster desktop processors of the same generation. Accordingly, when alignment algorithms previously optimized for desktop computing applications are ported to mobile applications, the processing time required for performing the alignment and generating a composite HDR image may greatly increase, which is not only undesirable from a performance standpoint, but may also negatively impact the user experience.